Technical Reports - Query Results

Pruning is an effective method for dealing with noise in Machine
Learning. Recently pruning algorithms, in particular Reduced
Error Pruning, have also attracted interest in the field of
Inductive Logic Programming. However, it has been shown that
these methods can be very inefficient, because most of the time is
wasted for generating clauses that explain noisy examples and
subsequently pruning these clauses. We introduce a new method
which searches for good theories in a top-down fashion to get a
better starting point for the pruning algorithm. Experiments show
that this approach can significantly lower the complexity of the
task as well as increase predictive accuracy.